Development and Application of a Gas−Liquid Contactor Model for

Environ. Sci. Technol. 1997, 31, 2791-2796

Development and Application of a Gas-Liquid Contactor Model for Simulating Advanced Oxidation Processes JOSEPH A. PEDIT,* KATHRYN J. IWAMASA,† CASS T. MILLER, AND WILLIAM H. GLAZE Department of Environmental Sciences and Engineering, CB 7400, 104 Rosenau Hall, University of North Carolina, Chapel Hill, North Carolina 27599-7400

Commercial and municipal applications of advanced oxidation processes have preceded a complete understanding of their chemical mechanisms. Understanding these processes is difficult because of the large number of chemical species and reactions involved. We developed a flexible and extensible, mechanistically based model to aid in understanding these processes. The model allows for multiple chemical species in each phase, multiple chemical reactions among species within each phase, and mass transfer between the gas and aqueous phases. We implemented a specific formulation of this model for the case of ozonehydrogen peroxide oxidation of trichloroethylene and tetrachloroethylene. This formulation was used to describe data from a full-scale demonstration plant and shown to provide a reasonably accurate representation.

Introduction Micropollutants frequently need to be removed from various industrial and municipal contaminated water streams. This removal has traditionally been accomplished using phasetransfer processes such as activated carbon adsorption or gas-phase stripping. Increasingly, micropollutants are removed using methods collectively known as advanced oxidation processes (1, 2). Advanced oxidation processes (AOPs) exploit the rapid reactivity of the hydroxyl radical (OH•), or other highly reactive radical species, to oxidize many organic species. The most widely used oxygen-based AOPs include ozone (O3) with ultraviolet radiation, O3 with hydrogen peroxide (H2O2), and H2O2 with ultraviolet radiation. The O3-H2O2 process uses H2O2 to accelerate the decomposition of O3 and form OH•. The process can be applied to oxidation of organic contaminants in industrial wastewater treatment and groundwater remediation, as well as for the control of taste- and odor-causing compounds and micropollutants in surface water treatment (3). Currently, drinking water ozonation facilities are being built to allow for the easy addition of H2O2. Approximately 40-50 potable water plants with the potential for O3-H2O2 capabilities will be operational by the end of this century (4). Commercial and municipal applications of the O3-H2O2 process as well as other AOPs have preceded a complete understanding of their chemical mechanisms. Indeed, some * Corresponding author fax: (919)966-7911; e-mail address: [email protected]. †Present address: Camp, Dresser, and McKee, Inc., 2100 River Edge Parkway, Suite 500, Atlanta, GA 30328.

S0013-936X(97)00061-8 CCC: $14.00

 1997 American Chemical Society

applications have been implemented without systematic studies of AOPs and their mechanisms, advantages and disadvantages, and without comparison to other technologies (5). A mathematical model is needed to aid in the understanding of AOPs and to facilitate the sound design and application of these processes. Such a model could be used to predict reactor performance with respect to micropollutant destruction and to determine the optimal O3 to H2O2 dose ratio for a desired treatment objective. Several items should be considered in the development of a model capable of simulating practical applications like the O3-H2O2 process. (a) The model should be based on the chemical mechanism behind the process, allowing the model to predict performance in different systems without refitting empirical parameters that only apply to a particular system. (b) Reactions may be limited by the mass transfer of O3 from the gas phase into the aqueous phase. It is necessary to account for mass transfer between the gas and aqueous phases in such systems to predict performance accurately (2). (c) The model should be applicable to a variety of reactor configurations with easily defined hydrodynamics. Reactors of interest range from batch-type reactors used in research to concurrent and countercurrent column-type reactors used in practical applications. (d) The model should predict transient behavior in response to changes in influent concentrations and other transient phenomena. (e) Several chemical species in AOPs are subject to speciation controlled by pH; to account for pH-controlled speciation, a set of algebraic constraints need to be imposed on the system of partial differential equations (PDEs) that describe the concentration changes in the system. (f) The model must be flexible to allow for the application of a single tool to a variety of AOPs, micropollutants, and background water characteristics. (g) The model must be extensible so that modifications in reaction mechanisms or rates that evolve as our understanding of these systems evolve can be easily incorporated into the modeling framework. No model reported in the literature to date satisfies this set of criteria. For example, early models (6-8) dealt only with batch-type reactors and steady-state solutions. Speciation has been handled by solving only for the steady-state solution, after pH-controlled speciations have become constant. Recent model developments (9) were not designed for transient solutions and included empirical models to describe mixing in the reactors. Additionally, none of these models allow for the easy addition of further mechanistic information, such as byproducts. The objectives of this work were (a) to develop an AOP model that meets the desirable criteria summarized above; (b) to show a specific example of a detailed AOP process formulation using the developed model; and (c) to apply the developed model to describe the results of data collected from a full-scale application.

Model Development Overview. The target class of problems requires description of flow, transport, and reaction phenomena of multiple inorganic and organic species in systems that range from small-scale laboratory to full-scale treatment operations. A flexible and extensible framework is necessary to resolve multiple types of AOPs and a wide range of conditions. Aspects of this problem that will be considered include fluid flow, species transport and reaction, and simulator design and implementation. Fluid Flow. A description of a reactor’s fluid flow dynamics is required before species transport and reaction can be described. The fluid velocities are described for all phases

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as functions of space and time throughout the reactor system. This description can be approached in several ways, but for many cases simplification from a complete solution of the Navier-Stokes equations can be used to provide a realistic description of the flow system. When it is possible to simplify with sufficient accuracy, a significant savings in computational effort results.

phase; zR,i is the location of the inlet for the R phase; and zR,o is the location of the outlet for the R phase. For concurrent systems, the inlet and outlet locations for each phase coincide, whereas for countercurrent systems, the inlet of one phase coincides with the outlet of the other phase. Initial conditions are specified as

For the applications considered in this work, several assumptions were made to simplify the resultant model: (a) concentration gradients perpendicular to the linear flow direction were neglected, reducing the system to one spatial dimension; (b) volume fraction, density, velocity, and dispersion were considered constant throughout the system for the aqueous and gas phases; (c) fluid flow was considered to be at steady state; (d) gas-phase bubbles were assumed to be of constant size (i.e., bubble size does not change in response to differences in fluid pressures, coalescence, or mass transfer). The above assumptions result in the specification of the system’s mean flow behavior in terms of reactor configuration, fluid flow rates, and volumetric fractions, which are all observable quantities. This approach is realistic and not overly restrictive for many systems of concern.

i in Ω CiR(z,0) ) CR,o

Species Transport and Reaction. The model formulation allows for accumulation, advection, dispersion, mass transfer between the gas and aqueous phases, chemical reactions described by mass action rate laws, and reversible chemical reactions considered to be at steady state described algebraically through equilibrium constants. The modeling approach does not account for reactions that occur in the boundary layer during mass transfer between the gas and aqueous phases. Ignoring the reactions in the boundary layer may lead to inaccurate results when rates of reaction are fast as compared to rates of transport through the boundary layer. The modeling approach also does not account for precipitation and dissolution processes. Species transport and reaction in both the aqueous and gas phases are modeled using a general PDE of the form

L

(CiR)

∂CiR )∂t

∂CiR - νR

∂z

∂2CiR + DR

+ ∂z2

(1 - 2δgR) i Igfa + θR

nRi

∑s

i

i R,jr R,j

in Ω × [0, T] (1)

j)1

where L is the differential-algebraic operator; CiR the concentration of species i in the R phase, where R is either a for the aqueous phase or g for the gas phase; t is time; vR is the R-phase velocity; z is the spatial coordinate; DR is the R-phase dispersion coefficient; θR is the R-phase volumetric fraction; δgR is the Kronecker delta function; Iigfa represents mass transfer of species i from the gas to the aqueous phase; j is an index; niR is the number of reactions involving species i i in the R phase; sR,j is a stoichiometric coefficient for reaction i j involving species i in the R phase; rR,j is a general rate expression for reaction j involving species i in the R phase; Ω ) [0, L] is the spatial domain of interest; L is the length of the domain; and [0, T] is the temporal domain of interest. The boundary conditions for the transport of species i in the R phase are i νRCR,s ) νRCiR - DR

∂CiR for z ) zR,i, 0 e t e T ∂z

∂CiR ) 0 for z ) zR,o, 0 e t e T ∂z

9

Species in the aqueous phase can be divided into three categories: nondissociating species, simple acids, and the hydrogen ion (H+) and hydroxide ion (OH-). The above PDE allows for mass transfer between the gas and aqueous phases for the ionized species (e.g., H+, OH-, or a conjugate base, A-, of a monoprotic acid HA), but the mass transfer of these species will not be allowed in our model formulation. Interphase Mass Transfer. The above formulation requires the specification of a constitutive relation form for Iig-a, which is done using the usual approach

i Igfa ) kiLa

(

Cig Hi

- Cia

(5)

Reactions. A complete formulation for any system requires the specification of the reactions for each species, i which are expressed as a set of expressions for rR,j . While these reactions may be generally specified as rational functions of species concentrations, the case of local equilibrium between species can lead to additional simplifications and deserves special consideration. For example, consider a monoprotic acid (other than water) in the aqueous phase subject to a reversible reaction of the form

HA T H+ + A-

(6)

If the forward and reverse reactions are designated as the first two reactions for HA and A-, then the first two reaction terms for HA and A- are 2

∑ j)1

2

∑s

A A sa,j ra,j ) -

-

HA HA a,j ra,j

H A ) kHACHA a - kH+,A-Ca Ca +

-

(7)

j)1

where kHA and kH+,A- are the forward and reverse reaction rate constants, respectively. These reaction rate constants are usually large so that the forward and reverse reactions are considered to be at steady state at any location and time. In this case, an equilibrium constant (KHA) is defined as

KHA )

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)

where kiL is the mass transfer coefficient for species i between the gas and aqueous phases; a is the specific interfacial area between the gas and aqueous phases; and Hi is the dimensionless Henry’s law constant for species i (i.e., Cig/Cia at equilibrium).

A CH a Ca +

i where CR,s is the source concentration of species i in the R

2792

i where CR,o is the initial concentration of species i in the R phase.

(2)

(3)

(4)

CHA a

-

(8)

Because the sum of the first two reaction terms for HA and A- differ only in sign, the need to account for the reversible

reactions considered to be at steady state can be eliminated by adding the governing PDEs for HA and A-, leading to

L

(CHA a )

(CAa )

∂CHA a

∂CAa

∂CAa

-

∂CAa

-

)-

(

)

nAa

∑s

A- Aa,j ra,j

∑

The above PDE and the algebraic constraint defined by the equilibrium constant (eq 8) represent the two equations needed to describe the dynamics of HA and A- fully. A similar approach can be used for multiprotic acids. For a diprotic acid, H2A, the governing equations for H2A, HA-, and A2- are combined. For a triprotic acid, H3A, the governing equations for H3A, H2A-, HA2-, and A3- are combined. The H+ and OH- concentrations may be resolved by enforcing the algebraic constraints that arise from electroneutrality and the equilibrium constant for water (KW). These constraints are given by m

i i a

)0

(10)

i)1

OH KW ) CH a Ca +

-

phase

influent concn

O3 H2O2 TCE PCE H+ alkalinity NOM

gas aqueous aqueous aqueous aqueous aqueous aqueous

1.748% by wt 2.5 mg/L 42 µg/L 45 µg/L pH 7.75 210 mg/L as CaCO3 0.70 mg/L as carbon

in Ω × [0, T] (9)

j)1

∑z C

species -

- νa - νa + ∂t ∂t ∂z ∂z HA nHA a kHA ∂2CHA ∂2CAa a L a Cg HA HA HA + Da + - Ca + sa,j ra,j + Da 2 2 HA θ j)1 ∂z ∂z H a +L

TABLE 1. Influent Concentrations

(11)

where m is the number of species in the aqueous phase, and zi is the charge of species i. Simulator Design and Implementation. We developed a computer code to solve the system of PDEs and algebraic constraints described above. The code allows the user to specify the number of desired chemical species in each phase, reactions among species within a phase, algebraic constraints imposed through electroneutrality and equilibrium constants in the aqueous phase, and mass transfer between the gas and aqueous phases. The governing system of equations was solved numerically. The method of lines (10) was used to reduce the system of PDEs to a system of ordinary differential equations (ODEs). The Bubnov-Galerkin finite element method (11) was used to approximate the spatial derivatives of the PDEs, and variable order Lagrange polynomials were used for the basis and test functions. The resulting system of ODEs and algebraic constraints were solved as function of time by a differential-algebraic equation solver developed for stiff systems (12), which is based on variable-order, fixed-leadingcoefficient, backward-difference formulas. The AOP problems we are attempting to solve are stiff because of the wide range of concentrations and reaction rate constants. For some problems, the convergence criteria and the number of spatial nodes need to be adjusted in order to achieve a converged solution.

Model Application The Los Angeles Department of Water and Power (LADWP) constructed a full-scale demonstration plant that uses an O3H2O2 AOP to treat groundwater that might be contaminated by chlorinated solvents such as trichloroethylene (TCE) and tetrachloroethylene (PCE). A series of experiments using groundwater spiked with TCE and PCE were conducted to evaluate the performance of the demonstration plant. An overview of the these experiments and a first-order reaction model interpretation of the results are described by Karimi and co-workers (13). The results from one of these experiments were used to test the model’s ability to simulate the complex chemistry involved in the O3-H2O2 process. Several

physical and chemical properties were needed to model the experimental results from the gas-liquid demonstration plant, including volumetric fractions for each phase, phase velocities, dispersion coefficients, influent concentrations, chemical reaction scheme, equilibrium constants, Henry’s law constants, and mass transfer coefficients. The reactor was operated in concurrent mode with O3 introduced in the gas phase and H2O2 introduced in the aqueous phase. The reactor had an inner diameter of 297 cm and a height of 381 cm. The upper 259 cm of the reactor contained 2.54-cm saddle-type packing. Complete reactor details are given by Karimi and co-workers (13). To simplify the modeling of the reactor’s performance, it was modeled as having uniform physical properties throughout the reactor. Volume fractions and velocities were calculated from the flow rates and the physical properties of the reactor and packing. Dispersion Coefficients. The aqueous-phase dispersion coefficient was determined by using the model to analyze the results of a nonreactive tracer test. Phase velocities during the tracer test were 18 and 2.4 cm/s for the gas and aqueous phases, respectively. Volumetric fractions were 0.021 and 0.82 for the gas and aqueous phases, respectively. A constant concentration of fluoride was fed into the reactor until the effluent concentration reached steady state, at which point the fluoride source was stopped. Fluoride concentration in the effluent was measured after the cessation of the fluoride source to the influent. The results of the tracer test are given elsewhere (13). The aqueous-phase dispersion coefficient was determined by least-squares regression between the effluent data and the model predictions. The aqueous-phase flow rate during the experiment with the O3-H2O2 process was the same as in the tracer experiment. However, the gas-phase velocity was reduced from 18 to 16 cm/s. It was assumed that the aqueous-phase dispersion coefficient estimated from the tracer test was adequate for modeling the experiment with the O3-H2O2 process. A value for the gas-phase dispersion coefficient had to be assumed because a gas-phase tracer test was not conducted. No correlations for estimating gas-phase dispersion coefficients in concurrent packed systems could be found, but gas-phase dispersion coefficients in concurrent packed systems should be smaller than in concurrent systems without packing (14). The gas-phase dispersion coefficient was estimated using a correlation developed for concurrent systems without packing (14). AOP Experiment. The details of the procedures used in the AOP experiments are given elsewhere (13), but we will give a brief summary. A compressor pumped a steady air flow through an O3 generator and into the reactor. The gas flow rate and influent O3 gas-phase concentration were monitored. Groundwater from a well located at the demonstration plant was used as influent to the reactor. The groundwater was spiked with H2O2, TCE, and PCE before delivery to the reactor, and influent TCE, PCE, and H2O2 aqueous-phase concentrations were monitored. Influent pH and alkalinity were also measured. Table 1 shows relevant influent concentrations for the experiment being used to evaluate the model. Ozone effluent concentrations were monitored in the gas and aqueous phases. Aqueous-phase concentrations of TCE

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TABLE 2. Mechanism for O3-H2O2 Oxidation of TCE and PCE no.

reaction Initiation Reactions

5.5 × 106 M-1 s-1 70 M-1 s-1

15 15

HO2- + O3 f O3•- + HO2• OH- + O3 f HO2- + O2

3 4 5

O2•- + O3 f O3•- + O2 O3•- + H+ f HO3• HO3• f OH• + O2

1.6 × 109 M-1 s-1 5.2 × 1010 M-1 s-1 1.1 × 105 s-1

16 16 16

Destruction and Scavenging Reactions O3 + TCE f products O3 + PCE f products OH• + TCE f products OH• + PCE f products OH• + HO2- f OH- + HO2• OH• + H2O2 f H2O + HO2• OH• + HCO3- f H2O + CO3•OH• + CO32- f OH- + CO3•OH• + O3 f HO2• + O2 CO3•- + H2O2 f HO2• + HCO3CO3•- + HO2- f O2•- + HCO3HO2• + HO2• f O2 + H2O2 O2•- + HO2• + H2O f O2 + H2O2 + OHCO3•- + CO3•- f products

17 M-1 s-1 0.1 M-1 s-1 4.0 × 109 M-1 s-1 2.0 × 109 M-1 s-1 7.5 × 109 M-1 s-1 2.7 × 107 M-1 s-1 8.5 × 106 M-1 s-1 3.9 × 108 M-1 s-1 1.1 × 108 M-1 s-1 8.0 × 105 M-1 s-1 5.6 × 107 M-1 s-1 8.3 × 105 M-1 s-1 9.7 × 107 M-1 s-1 2.2 × 106 M-1 s-1

17 17 18 19 20 21 21 21 22 23 23 24 24 25

6 7 8 9 10 11 12 13 14 15 16 17 18 19

TABLE 3. Equilibrium Constants reaction H2O2 T HO2 + HO2• T O2•- + H+ H2CO3 T HCO3- + H+ HCO3- T CO32- + H+ H2O T OH- + H+ -

H+

pK

ref

11.7 4.8 6.3 10.3 14

23 15 26 26 26

and PCE were monitored at five locations along the length of the reactor. Samples were collected at 15-min intervals until steady state was achieved. The O3-H2O2 process relies on a series of initiation and propagation reactions involving H2O2 and O3 to generate OH•. The mechanism for the oxidation of TCE and PCE by the O3-H2O2 process in the aqueous phase is shown in Table 2. The mechanism shown is a modified version of the mechanism proposed by Glaze and Kang (8). TCE and PCE are destroyed by two pathways in this process: direct oxidation by O3 (reactions 6 and 7) and oxidation by OH• (reactions 8 and 9). The current mechanism includes several reactions that were not in the mechanism proposed by Glaze and Kang (8). The carbonate radical (CO3•-) reaction with the conjugate base of hydrogen peroxide (HO2-) has been added (reaction 16). Three radical-radical recombination reactions have been added (reactions 17-19). The recombination of the hydroperoxyl radical (HO2•) with itself and with its conjugate base, superoxide radical (O2•-), are shown in reactions 17 and 18, respectively. The recombination of CO3•- with itself is shown in reaction 19. All of the chemical species involved in the reactions listed in Table 2 were accounted for in the model. The species that undergo dissociation include hydrogen peroxide, hydroperoxyl radical, bicarbonate, carbonate, and water. Equilibrium constants of these species are listed in Table 3. The species accounted for in the gas phase included TCE, PCE, O3, and CO2. No chemical reactions were specified for the gas phase. Mass transfer between the gas and aqueous phases for these species had to be considered. Henry’s law constants for these species were assigned values of 0.41, 0.63, 3.2, and 1.1 for TCE, PCE, O3, and CO2, respectively (26-28). Ideally, the mass transfer coefficients for these species would have been determined by independent experiments. For

9

ref

1 2

Propagation Reactions

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rate constant

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 31, NO. 10, 1997

example, the ozone mass transfer coefficient would have been determined by conducting an experiment in the reactor with ozone demand free water. Such experiments were not available. We could not find any suitable mass transfer correlations for packed bed reactors operated in upflow concurrent mode. For these reasons, the mass transfer coefficients for these species were determined by least-squares regression between measured and model-predicted, logtransformed, steady-state, aqueous-phase concentrations of TCE and PCE at the five locations along the length of the reactor. In order to minimize the number of estimated parameters, it was assumed that the four species had the same mass transfer coefficient.

Results and Discussion The optimal aqueous-phase dispersion coefficient, as determined by least-squares regression of the nonreactive tracer data, was 77 cm2/s. This value corresponds to an aqueousphase system Peclet number (Pea ) vaL/Da) of 12. A correlation for estimating aqueous-phase dispersion coefficients in concurrent packed systems (14) yielded an estimate of 75 cm2/s, which is in excellent agreement with the optimal dispersion coefficient. Based on the correlation developed for concurrent systems without packing (14), the gas-phase dispersion coefficient was estimated to be 3600 cm2/s, corresponding to a gasphase system Peclet number (Peg ) vgL/Dg) of 1.7. A sensitivity analysis (discussed later) indicated that the mass-transfer coefficient estimated from the results of the O3-H2O2 process experiment were sensitive to the assumed value of the gasphase dispersion coefficient. The least-squares regression of the data from the O3H2O2 process experiment yielded an estimate of the optimal mass transfer coefficient of 4.25 × 10-3 1/s. The experimental results and the model simulation using the optimal masstransfer coefficient are shown in Figure 1. The model simulation provides a reasonable description of the TCE and PCE data. The effectiveness of TCE removal by reaction with OH• compared to removal by reaction with O3 is demonstrated in Figure 2. The model predicts that the TCE removal rate by reaction with OH• is approximately 4 orders of magnitude faster than by reaction with O3 even though OH• concentrations are approximately 4 orders of magnitude lower than O3 concentrations. This difference is a result of the 8 orders of

TABLE 4. Effluent Concentrations

O3 gas-phase concn (% by wt) O3 aqueous-phase concn (mg/L) O3 utilization (%) pH alkalinity (mg/L as CaCO3) rate const for NOM reaction (M-1 s-1) mass transfer coefficient (1/s) sum of squared residuals

measured

model without NOM reaction

model with NOM + OH• f ΨO2•- + products

0.202 0 88.4 7.78 206 not applicable not applicable not applicable

0.594 7.30 × 10-3 65.8 7.81 209 not applicable 4.25 × 10-3 0.138

0.327 3.45 × 10-3 81.2 7.85 209 2.3 × 108 7.69 × 10-3 0.128

FIGURE 1. Observed and model-predicted aqueous-phase concentrations of TCE and PCE in a full-scale demonstration plant using the O3-H2O2 process.

FIGURE 2. Model-predicted (A) aqueous-phase concentrations of O3 and OH•, and (B) TCE removal rates by reaction with O3 and OH•. magnitude difference in rate constants. The difference in removal rates for PCE by reaction with OH• and O3 is even greater (∼106)sanother reflection of the substantial difference in rate constants.

FIGURE 3. Sensitivity of the optimal mass transfer coefficient and sum of the squared errors (SSE) to changes in the gas-phase dispersion coefficient. Recall that the gas-phase dispersion coefficient was assumed to be 3600 cm2/s (Peg ) 1.7). A sensitivity analysis (shown in Figure 3) was conducted to determine how the optimal mass transfer coefficient and model fit (as measured by the sum of the squared errors, SSE) would change at different assumed values for the gas-phase Peclet number. The model fit was insensitive to increases in the gas-phase Peclet number (i.e., decreases in the gas-phase dispersion coefficient). The SSE decreased 7.4% as Peg was increased from 1.7 to 100. The optimal mass-transfer coefficient was more sensitive than SSE to increasing the gas-phase Peclet number. The optimal mass transfer coefficient declined 31% as Peg was increased from 1.7 to 100, most of the decline occurring before Peg was increased to 10. The decline in mass-transfer coefficient, with little change in the model fit as the gas-phase Peclet number was increased, indicates that the system becomes more efficient in the destruction of TCE and PCE, which is expected as the gas phase approaches plug flow conditions. Although the optimal model fit shown in Figure 1 provides a reasonable description of the TCE and PCE data, the model did not accurately simulate the observed O3 gas-phase effluent concentration and the overall O3 utilization. Table 4 shows a comparison between the observed and model predicted gas- and aqueous-phase effluent O3 concentrations, pH, and alkalinity as well as the overall O3 utilization. The model predicts three times as much gas-phase O3 in the effluent than was observed and predicts an ozone utilization of 65.8% as compared to an experimentally observed ozone utilization of 88.4% (i.e., the model accounts for 74% of the observed ozone utilization). Other chemical species that were present in the influent may have contributed to O3 consumption. Bromide, which was present in the influent at concentrations ranging from 0.21 to 0.24 mg/L, reacts with O3 and OH• to form bromate and several intermediates (29). Bromate was observed in the effluent at concentrations ranging from 0.094 to 0.11 mg/L (13), which indicates that the presence of

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bromide would not contribute substantially to O3 utilization. Natural organic matter (NOM), which was observed in the influent at concentrations ranging from 0.53 to 0.85 mg/L as carbon, can react with OH• (30). The reaction is given by

NOM + OH• f ΨO2•- + products

(12)

where Ψ is the fraction of OH• consumed leading to O2•production. Additional model simulations were conducted to determine if the reaction of NOM with OH• could account for a substantial portion of the experimentally observed ozone utilization. The rate constant for the reaction of NOM with OH• was set to 2.3 × 108 M-1 s-1, the value experimentally observed by Peyton (31). It was assumed that the reaction between NOM and OH• acted only as a promoter of ozone decomposition (i.e., the assumed reaction produces superoxide radical that reacts with ozone to produce OH• eventually). This assumption was accomplished by setting Ψ equal to 1. The average influent NOM concentration of 0.70 mg/L as carbon (5.8 × 10-5 mol/L as carbon) was used for the simulations. The optimal mass transfer coefficient was determined by regression as described previously. Optimal parameter values are given in Table 4. The model predicted TCE and PCE concentrations for this scenario (not shown) are similar to those shown in Figure 1. The optimal mass transfer coefficient was 1.8 times larger than the optimal mass transfer coefficient for the model without a NOM reaction, which is expected because of the additional oxidant demand arising from the presence of NOM. Inclusion of the NOM reaction increased the predicted ozone utilization to 81.2%, which is 92% of the observed ozone utilization. The ease with which an additional chemical species (e.g., NOM) was added to an already complex reaction mechanism demonstrates the flexibility of the model. Such flexibility allows the model to be used for not only the O3-H2O2 process but other complex processes as well. We are currently testing the capabilities of the model by using it to simulate the results of a large number of experiments that were conducted at the LADWP full-scale demonstration plant. The model is also being used to simulate Fenton reactions at low dissolved iron concentrations, the experimental results from a laboratoryscale reactor used to investigate the O3 oxidation of NOM, and the experimental results from a pilot-scale reactor used to investigate the O3-H2O2 oxidation of munitions-contaminated groundwater.

Literature Cited (1) Glaze, W. H.; Kang, J.; Chapin, D. Ozone Sci. Eng. 1987, 9, 335352. (2) Haas, C. N.; Vamos R. J. Hazardous and Industrial Waste Treatment; Prentice-Hall: Englewood Cliffs, NJ, 1995. (3) Glaze, W. H.; Raymond R.; Chauncey, W.; et al. J. Am. Water Works Assoc. 1990, 79-84. (4) Rice, R. G. Personal communication, May 3, 1995.

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Received for review January 27, 1997. Revised manuscript received June 11, 1997. Accepted June 16, 1997.X ES9700616 X

Abstract published in Advance ACS Abstracts, August 1, 1997.